Rewrites column.__setitem__, Use boolean_mask_scatter (#10202)

closes #8667 This PR rewrites `column.__setitem__` and calls `boolean_mask_scatter` if keys and values meet some criteria. Benchmark shows in low-order problem size (10K ish), there are 30% speed up for aligned values and 10% for unaligned values. Note standard deviation of the unaligned case is quite high after refactor. For larger problem sizes performance is rather unaffected. Benchmarks: <details> <summary>10K</summary> ``` ---------------------------------------------------------------- benchmark 'boolean_mask_col_aligned': 2 tests ----------------------------------------------------------------- Name (time in ms) Min Max Mean StdDev Median IQR Outliers Rounds -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- column_setitem[boolean_mask_col_aligned] (afte) 1.2809 (1.0) 1.6781 (1.0) 1.3064 (1.0) 0.0364 (1.0) 1.2996 (1.0) 0.0137 (1.0) 22;34 761 column_setitem[boolean_mask_col_aligned] (befo) 1.7024 (1.33) 2.3863 (1.42) 1.7270 (1.32) 0.0523 (1.43) 1.7187 (1.32) 0.0138 (1.01) 20;31 563 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------------------------------------------------------------------- benchmark 'boolean_mask_col_unaligned': 2 tests -------------------------------------------------------------------------- Name (time in us) Min Max Mean StdDev Median IQR Outliers Rounds ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- column_setitem[boolean_mask_col_unaligned] (afte) 972.3390 (1.0) 1,520.7559 (1.29) 1,008.6033 (1.0) 77.0920 (9.45) 984.0354 (1.0) 13.2429 (1.45) 83;132 984 column_setitem[boolean_mask_col_unaligned] (befo) 1,106.3821 (1.14) 1,179.5689 (1.0) 1,118.7759 (1.11) 8.1539 (1.0) 1,116.2200 (1.13) 9.1530 (1.0) 149;30 874 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------- benchmark 'boolean_mask_scalar': 2 tests --------------------------------------------------------------------- Name (time in us) Min Max Mean StdDev Median IQR Outliers Rounds ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ column_setitem[boolean_mask_scalar] (afte) 532.2532 (1.0) 605.4689 (1.0) 542.2607 (1.0) 11.1111 (1.10) 537.9058 (1.0) 5.2921 (1.0) 178;213 1461 column_setitem[boolean_mask_scalar] (befo) 770.1530 (1.45) 863.1549 (1.43) 781.4038 (1.44) 10.0834 (1.0) 778.3370 (1.45) 7.0044 (1.32) 114;90 1219 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ ----------------------------------------------------------------- benchmark 'integer_scatter_map_col': 2 tests ---------------------------------------------------------------- Name (time in ms) Min Max Mean StdDev Median IQR Outliers Rounds ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- column_setitem[integer_scatter_map_col] (afte) 1.4785 (1.0) 1.9170 (1.21) 1.5176 (1.01) 0.0438 (3.91) 1.5098 (1.00) 0.0171 (1.37) 18;26 644 column_setitem[integer_scatter_map_col] (befo) 1.4882 (1.01) 1.5802 (1.0) 1.5084 (1.0) 0.0112 (1.0) 1.5068 (1.0) 0.0124 (1.0) 140;22 650 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------- benchmark 'integer_scatter_map_scalar': 2 tests ---------------------------------------------------------------------- Name (time in us) Min Max Mean StdDev Median IQR Outliers Rounds --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- column_setitem[integer_scatter_map_scalar] (afte) 878.2479 (1.0) 1,343.0519 (1.39) 897.1496 (1.01) 27.5109 (3.23) 892.9770 (1.00) 7.8208 (1.0) 29;60 1074 column_setitem[integer_scatter_map_scalar] (befo) 879.3280 (1.00) 966.9410 (1.0) 890.8573 (1.0) 8.5287 (1.0) 888.7504 (1.0) 8.4981 (1.09) 171;50 1086 --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ---------------------------------------------------------------------- benchmark 'stride-1_slice_col': 2 tests ----------------------------------------------------------------------- Name (time in us) Min Max Mean StdDev Median IQR Outliers Rounds -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- column_setitem[stride-1_slice_col] (afte) 752.6411 (1.0) 852.3620 (1.0) 775.1790 (1.0) 10.9726 (1.0) 772.6796 (1.0) 14.1604 (1.32) 245;23 1152 column_setitem[stride-1_slice_col] (befo) 974.8179 (1.30) 1,307.2360 (1.53) 991.1287 (1.28) 25.8696 (2.36) 985.5330 (1.28) 10.6919 (1.0) 30;51 763 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------------------------------------------------------------- benchmark 'stride-1_slice_scalar': 2 tests ------------------------------------------------------------------- Name (time in us) Min Max Mean StdDev Median IQR Outliers Rounds ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- column_setitem[stride-1_slice_scalar] (afte) 87.3711 (1.16) 134.7861 (1.0) 89.7566 (1.15) 2.4861 (1.0) 89.5601 (1.16) 1.6061 (1.13) 95;87 2106 column_setitem[stride-1_slice_scalar] (befo) 75.3789 (1.0) 136.7659 (1.01) 78.0297 (1.0) 4.8186 (1.94) 77.0842 (1.0) 1.4230 (1.0) 122;184 2403 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------- benchmark 'stride-2_slice_col': 2 tests ---------------------------------------------------------------------- Name (time in us) Min Max Mean StdDev Median IQR Outliers Rounds ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ column_setitem[stride-2_slice_col] (afte) 684.7882 (1.02) 972.5131 (1.01) 712.5983 (1.04) 54.4972 (3.04) 693.8996 (1.02) 10.3808 (1.18) 109;163 1338 column_setitem[stride-2_slice_col] (befo) 672.3758 (1.0) 964.4001 (1.0) 684.2917 (1.0) 17.9163 (1.0) 679.5955 (1.0) 8.7875 (1.0) 85;106 1368 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ --------------------------------------------------------------------- benchmark 'stride-2_slice_scalar': 2 tests -------------------------------------------------------------------- Name (time in us) Min Max Mean StdDev Median IQR Outliers Rounds ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- column_setitem[stride-2_slice_scalar] (afte) 302.0421 (1.04) 374.3470 (1.0) 307.5677 (1.04) 4.3768 (1.0) 306.4690 (1.04) 2.4854 (1.17) 258;253 2532 column_setitem[stride-2_slice_scalar] (befo) 290.4800 (1.0) 378.1999 (1.01) 295.3950 (1.0) 4.6778 (1.07) 294.1729 (1.0) 2.1292 (1.0) 273;324 2977 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ``` </details> <details> <summary> 1M </summary> ``` ------------------------------ benchmark 'boolean_mask_col_aligned': 2 tests ------------------------------ Name (time in ms) Min Max Mean ----------------------------------------------------------------------------------------------------------- column_setitem[boolean_mask_col_aligned] (afte) 75.3847 (1.0) 79.0559 (1.0) 76.2878 (1.0) column_setitem[boolean_mask_col_aligned] (befo) 76.3708 (1.01) 79.7394 (1.01) 77.0892 (1.01) ----------------------------------------------------------------------------------------------------------- ------------------------------ benchmark 'boolean_mask_col_unaligned': 2 tests ------------------------------ Name (time in ms) Min Max Mean ------------------------------------------------------------------------------------------------------------- column_setitem[boolean_mask_col_unaligned] (afte) 46.5199 (1.0) 48.3434 (1.0) 46.9222 (1.0) column_setitem[boolean_mask_col_unaligned] (befo) 46.6314 (1.00) 48.5938 (1.01) 47.1492 (1.00) ------------------------------------------------------------------------------------------------------------- ------------------------------ benchmark 'boolean_mask_scalar': 2 tests ------------------------------ Name (time in ms) Min Max Mean ------------------------------------------------------------------------------------------------------ column_setitem[boolean_mask_scalar] (afte) 17.0548 (1.0) 17.8006 (1.0) 17.5430 (1.0) column_setitem[boolean_mask_scalar] (befo) 18.4329 (1.08) 18.6918 (1.05) 18.5073 (1.05) ------------------------------------------------------------------------------------------------------ -------------------------------- benchmark 'integer_scatter_map_col': 2 tests ------------------------------- Name (time in ms) Min Max Mean ------------------------------------------------------------------------------------------------------------- column_setitem[integer_scatter_map_col] (afte) 115.7189 (1.01) 120.0585 (1.0) 116.6452 (1.0) column_setitem[integer_scatter_map_col] (befo) 114.7481 (1.0) 122.7263 (1.02) 117.5000 (1.01) ------------------------------------------------------------------------------------------------------------- ------------------------------ benchmark 'integer_scatter_map_scalar': 2 tests ------------------------------ Name (time in ms) Min Max Mean ------------------------------------------------------------------------------------------------------------- column_setitem[integer_scatter_map_scalar] (afte) 57.9951 (1.0) 62.2284 (1.0) 59.8864 (1.0) column_setitem[integer_scatter_map_scalar] (befo) 60.9071 (1.05) 62.2952 (1.00) 61.6422 (1.03) ------------------------------------------------------------------------------------------------------------- ------------------------------ benchmark 'stride-1_slice_col': 2 tests ------------------------------ Name (time in ms) Min Max Mean ----------------------------------------------------------------------------------------------------- column_setitem[stride-1_slice_col] (afte) 56.9203 (1.0) 58.0924 (1.0) 57.4940 (1.0) column_setitem[stride-1_slice_col] (befo) 58.1888 (1.02) 59.2996 (1.02) 58.5722 (1.02) ----------------------------------------------------------------------------------------------------- -------------------------------- benchmark 'stride-1_slice_scalar': 2 tests ------------------------------- Name (time in us) Min Max Mean ----------------------------------------------------------------------------------------------------------- column_setitem[stride-1_slice_scalar] (afte) 287.1200 (1.08) 515.4130 (1.24) 298.1191 (1.09) column_setitem[stride-1_slice_scalar] (befo) 265.6982 (1.0) 415.9641 (1.0) 273.5206 (1.0) ----------------------------------------------------------------------------------------------------------- ------------------------------ benchmark 'stride-2_slice_col': 2 tests ------------------------------ Name (time in ms) Min Max Mean ----------------------------------------------------------------------------------------------------- column_setitem[stride-2_slice_col] (afte) 29.1543 (1.01) 31.0217 (1.00) 29.6341 (1.00) column_setitem[stride-2_slice_col] (befo) 28.9718 (1.0) 30.8824 (1.0) 29.5045 (1.0) ----------------------------------------------------------------------------------------------------- --------------------------------- benchmark 'stride-2_slice_scalar': 2 tests -------------------------------- Name (time in us) Min Max Mean ------------------------------------------------------------------------------------------------------------- column_setitem[stride-2_slice_scalar] (afte) 780.7089 (1.00) 1,407.5749 (1.0) 817.0583 (1.0) column_setitem[stride-2_slice_scalar] (befo) 777.0571 (1.0) 2,036.4628 (1.45) 832.0608 (1.02) ------------------------------------------------------------------------------------------------------------- ``` </details> Authors: - Michael Wang (https://github.com/isVoid) Approvers: - Bradley Dice (https://github.com/bdice) - Vyas Ramasubramani (https://github.com/vyasr) URL: #10202
rapidsai · Feb 23, 2022 · a72479f · a72479f
1 parent 496f452
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diff --git a/python/cudf/cudf/_lib/copying.pyx b/python/cudf/cudf/_lib/copying.pyx
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
 
 import pickle
 
@@ -537,11 +537,11 @@ def copy_if_else(object lhs, object rhs, Column boolean_mask):
                 as_device_scalar(lhs), as_device_scalar(rhs), boolean_mask)
 
 
-def _boolean_mask_scatter_table(input_table, target_table,
-                                Column boolean_mask):
+def _boolean_mask_scatter_columns(list input_columns, list target_columns,
+                                  Column boolean_mask):
 
-    cdef table_view input_table_view = table_view_from_columns(input_table)
-    cdef table_view target_table_view = table_view_from_columns(target_table)
+    cdef table_view input_table_view = table_view_from_columns(input_columns)
+    cdef table_view target_table_view = table_view_from_columns(target_columns)
     cdef column_view boolean_mask_view = boolean_mask.view()
 
     cdef unique_ptr[table] c_result
@@ -555,14 +555,10 @@ def _boolean_mask_scatter_table(input_table, target_table,
             )
         )
 
-    return data_from_unique_ptr(
-        move(c_result),
-        column_names=target_table._column_names,
-        index_names=target_table._index._column_names
-    )
+    return columns_from_unique_ptr(move(c_result))
 
 
-def _boolean_mask_scatter_scalar(list input_scalars, target_table,
+def _boolean_mask_scatter_scalar(list input_scalars, list target_columns,
                                  Column boolean_mask):
 
     cdef vector[reference_wrapper[constscalar]] input_scalar_vector
@@ -571,7 +567,7 @@ def _boolean_mask_scatter_scalar(list input_scalars, target_table,
     for scl in input_scalars:
         input_scalar_vector.push_back(reference_wrapper[constscalar](
             scl.get_raw_ptr()[0]))
-    cdef table_view target_table_view = table_view_from_columns(target_table)
+    cdef table_view target_table_view = table_view_from_columns(target_columns)
     cdef column_view boolean_mask_view = boolean_mask.view()
 
     cdef unique_ptr[table] c_result
@@ -585,29 +581,37 @@ def _boolean_mask_scatter_scalar(list input_scalars, target_table,
             )
         )
 
-    return data_from_unique_ptr(
-        move(c_result),
-        column_names=target_table._column_names,
-        index_names=target_table._index._column_names
-    )
+    return columns_from_unique_ptr(move(c_result))
 
 
-# TODO: This function is currently unused but should be used in
-# ColumnBase.__setitem__, see https://github.com/rapidsai/cudf/issues/8667.
-def boolean_mask_scatter(object input, target_table,
+def boolean_mask_scatter(list input_, list target_columns,
                          Column boolean_mask):
+    """Copy the target columns, replacing masked rows with input data.
+
+    The ``input_`` data can be a list of columns or as a list of scalars.
+    A list of input columns will be used to replace corresponding rows in the
+    target columns for which the boolean mask is ``True``. For the nth ``True``
+    in the boolean mask, the nth row in ``input_`` is used to replace. A list
+    of input scalars will replace all rows in the target columns for which the
+    boolean mask is ``True``.
+    """
+    if len(input_) != len(target_columns):
+        raise ValueError("Mismatched number of input and target columns.")
+
+    if len(input_) == 0:
+        return []
 
-    if isinstance(input, cudf.core.frame.Frame):
-        return _boolean_mask_scatter_table(
-            input,
-            target_table,
+    if isinstance(input_[0], Column):
+        return _boolean_mask_scatter_columns(
+            input_,
+            target_columns,
             boolean_mask
         )
     else:
-        scalar_list = [as_device_scalar(i) for i in input]
+        scalar_list = [as_device_scalar(i) for i in input_]
         return _boolean_mask_scatter_scalar(
             scalar_list,
-            target_table,
+            target_columns,
             boolean_mask
         )
 

diff --git a/python/cudf/cudf/core/column/column.py b/python/cudf/cudf/core/column/column.py
@@ -81,6 +81,9 @@
 from cudf.utils.utils import NotIterable, mask_dtype
 
 T = TypeVar("T", bound="ColumnBase")
+# TODO: This workaround allows type hints for `slice`, since `slice` is a
+# method in ColumnBase.
+Slice = TypeVar("Slice", bound=slice)
 
 
 class ColumnBase(Column, Serializable, NotIterable):
@@ -317,22 +320,24 @@ def _fill(
         if end <= begin or begin >= self.size:
             return self if inplace else self.copy()
 
-        fill_scalar = as_device_scalar(fill_value, self.dtype)
+        # Constructing a cuDF scalar can cut unnecessary DtoH copy if
+        # the scalar is None when calling `is_valid`.
+        slr = cudf.Scalar(fill_value, dtype=self.dtype)
 
         if not inplace:
-            return libcudf.filling.fill(self, begin, end, fill_scalar)
+            return libcudf.filling.fill(self, begin, end, slr.device_value)
 
         if is_string_dtype(self.dtype):
             return self._mimic_inplace(
-                libcudf.filling.fill(self, begin, end, fill_scalar),
+                libcudf.filling.fill(self, begin, end, slr.device_value),
                 inplace=True,
             )
 
-        if fill_value is None and not self.nullable:
+        if not slr.is_valid() and not self.nullable:
             mask = create_null_mask(self.size, state=MaskState.ALL_VALID)
             self.set_base_mask(mask)
 
-        libcudf.filling.fill_in_place(self, begin, end, fill_scalar)
+        libcudf.filling.fill_in_place(self, begin, end, slr.device_value)
 
         return self
 
@@ -491,82 +496,119 @@ def __getitem__(self, arg) -> Union[ScalarLike, ColumnBase]:
 
     def __setitem__(self, key: Any, value: Any):
         """
-        Set the value of self[key] to value.
+        Set the value of ``self[key]`` to ``value``.
 
-        If value and self are of different types,
-        value is coerced to self.dtype
+        If ``value`` and ``self`` are of different types, ``value`` is coerced
+        to ``self.dtype``. Assumes ``self`` and ``value`` are index-aligned.
         """
 
+        # Normalize value to scalar/column
+        value_normalized = (
+            cudf.Scalar(value, dtype=self.dtype)
+            if is_scalar(value)
+            else as_column(value, dtype=self.dtype)
+        )
+
+        out: Optional[ColumnBase]  # If None, no need to perform mimic inplace.
         if isinstance(key, slice):
-            key_start, key_stop, key_stride = key.indices(len(self))
-            if key_start < 0:
-                key_start = key_start + len(self)
-            if key_stop < 0:
-                key_stop = key_stop + len(self)
-            if key_start >= key_stop:
-                return self.copy()
-            if (key_stride is None or key_stride == 1) and is_scalar(value):
-                return self._fill(value, key_start, key_stop, inplace=True)
-            if key_stride != 1 or key_stride is not None or is_scalar(value):
-                key = arange(
-                    start=key_start,
-                    stop=key_stop,
-                    step=key_stride,
-                    dtype=cudf.dtype(np.int32),
-                )
-                nelem = len(key)
-            else:
-                nelem = abs(key_stop - key_start)
+            out = self._scatter_by_slice(key, value_normalized)
         else:
             key = as_column(key)
-            if is_bool_dtype(key.dtype):
-                if not len(key) == len(self):
-                    raise ValueError(
-                        "Boolean mask must be of same length as column"
-                    )
-                key = arange(len(self))[key]
-                if hasattr(value, "__len__") and len(value) == len(self):
-                    value = as_column(value)[key]
-            nelem = len(key)
+            if not isinstance(key, cudf.core.column.NumericalColumn):
+                raise ValueError(f"Invalid scatter map type {key.dtype}.")
+            out = self._scatter_by_column(key, value_normalized)
 
-        if is_scalar(value):
-            value = cudf.Scalar(value, dtype=self.dtype)
-        else:
-            if len(value) != nelem:
-                msg = (
-                    f"Size mismatch: cannot set value "
-                    f"of size {len(value)} to indexing result of size "
-                    f"{nelem}"
+        if out:
+            self._mimic_inplace(out, inplace=True)
+
+    def _scatter_by_slice(
+        self, key: Slice, value: Union[cudf.core.scalar.Scalar, ColumnBase]
+    ) -> Optional[ColumnBase]:
+        """If this function returns None, it's either a no-op (slice is empty),
+        or the inplace replacement is already performed (fill-in-place).
+        """
+        start, stop, step = key.indices(len(self))
+        if start >= stop:
+            return None
+        num_keys = (stop - start) // step
+
+        self._check_scatter_key_length(num_keys, value)
+
+        if step == 1:
+            if isinstance(value, cudf.core.scalar.Scalar):
+                return self._fill(value, start, stop, inplace=True)
+            else:
+                return libcudf.copying.copy_range(
+                    value, self, 0, num_keys, start, stop, False
                 )
-                raise ValueError(msg)
-            value = as_column(value).astype(self.dtype)
 
-        if (
-            isinstance(key, slice)
-            and (key_stride == 1 or key_stride is None)
-            and not is_scalar(value)
-        ):
+        # step != 1, create a scatter map with arange
+        scatter_map = arange(
+            start=start, stop=stop, step=step, dtype=cudf.dtype(np.int32),
+        )
 
-            out = libcudf.copying.copy_range(
-                value, self, 0, nelem, key_start, key_stop, False
-            )
+        return self._scatter_by_column(scatter_map, value)
+
+    def _scatter_by_column(
+        self,
+        key: cudf.core.column.NumericalColumn,
+        value: Union[cudf.core.scalar.Scalar, ColumnBase],
+    ) -> ColumnBase:
+        if is_bool_dtype(key.dtype):
+            # `key` is boolean mask
+            if len(key) != len(self):
+                raise ValueError(
+                    "Boolean mask must be of same length as column"
+                )
+            if isinstance(value, ColumnBase) and len(self) == len(value):
+                # Both value and key are aligned to self. Thus, the values
+                # corresponding to the false values in key should be
+                # ignored.
+                value = value.apply_boolean_mask(key)
+                # After applying boolean mask, the length of value equals
+                # the number of elements to scatter, we can skip computing
+                # the sum of ``key`` below.
+                num_keys = len(value)
+            else:
+                # Compute the number of element to scatter by summing all
+                # `True`s in the boolean mask.
+                num_keys = key.sum()
         else:
-            try:
-                if not isinstance(key, Column):
-                    key = as_column(key)
-                if not is_scalar(value) and not isinstance(value, Column):
-                    value = as_column(value)
-                out = libcudf.copying.scatter(
+            # `key` is integer scatter map
+            num_keys = len(key)
+
+        self._check_scatter_key_length(num_keys, value)
+
+        try:
+            if is_bool_dtype(key.dtype):
+                return libcudf.copying.boolean_mask_scatter(
+                    [value], [self], key
+                )[0]._with_type_metadata(self.dtype)
+            else:
+                return libcudf.copying.scatter(
                     value, key, self
                 )._with_type_metadata(self.dtype)
-            except RuntimeError as e:
-                if "out of bounds" in str(e):
-                    raise IndexError(
-                        f"index out of bounds for column of size {len(self)}"
-                    ) from e
-                raise
+        except RuntimeError as e:
+            if "out of bounds" in str(e):
+                raise IndexError(
+                    f"index out of bounds for column of size {len(self)}"
+                ) from e
+            raise
 
-        self._mimic_inplace(out, inplace=True)
+    def _check_scatter_key_length(
+        self, num_keys: int, value: Union[cudf.core.scalar.Scalar, ColumnBase]
+    ):
+        """`num_keys` is the number of keys to scatter. Should equal to the
+        number of rows in ``value`` if ``value`` is a column.
+        """
+        if isinstance(value, ColumnBase):
+            if len(value) != num_keys:
+                msg = (
+                    f"Size mismatch: cannot set value "
+                    f"of size {len(value)} to indexing result of size "
+                    f"{num_keys}"
+                )
+                raise ValueError(msg)
 
     def fillna(
         self: T,
@@ -2247,7 +2289,7 @@ def arange(
     stop: Union[int, float] = None,
     step: Union[int, float] = 1,
     dtype=None,
-) -> ColumnBase:
+) -> cudf.core.column.NumericalColumn:
     """
     Returns a column with evenly spaced values within a given interval.
 

diff --git a/python/cudf/cudf/core/df_protocol.py b/python/cudf/cudf/core/df_protocol.py
@@ -1,3 +1,5 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
 import collections
 import enum
 from typing import (

diff --git a/python/cudf/cudf/tests/test_df_protocol.py b/python/cudf/cudf/tests/test_df_protocol.py
@@ -1,3 +1,5 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+
 from typing import Any, Tuple
 
 import cupy as cp
@@ -27,13 +29,19 @@ def assert_buffer_equal(buffer_and_dtype: Tuple[_CuDFBuffer, Any], cudfcol):
     )
     # check that non null values are the equals as nulls are represented
     # by sentinel values in the buffer.
-    non_null_idxs = cudf.Series(cudfcol) != cudf.NA
+    # FIXME: In gh-10202 some minimal fixes were added to unblock CI. But
+    # currently only non-null values are compared, null positions are
+    # unchecked.
+    non_null_idxs = ~cudf.Series(cudfcol).isna()
     assert_eq(col_from_buf[non_null_idxs], cudfcol[non_null_idxs])
 
     if dtype[0] != _DtypeKind.BOOL:
-        array_from_dlpack = cp.fromDlpack(buf.__dlpack__())
-        col_array = cp.asarray(cudfcol.data_array_view)
-        assert_eq(array_from_dlpack.flatten(), col_array.flatten())
+        array_from_dlpack = cp.fromDlpack(buf.__dlpack__()).get()
+        col_array = cp.asarray(cudfcol.data_array_view).get()
+        assert_eq(
+            array_from_dlpack[non_null_idxs.to_numpy()].flatten(),
+            col_array[non_null_idxs.to_numpy()].flatten(),
+        )
     else:
         pytest.raises(TypeError, buf.__dlpack__)