Refactor cython interface: copying.pyx

python/cudf/cudf/_lib/copying.pyx

@@ -38,6 +38,7 @@ from cudf._lib.cpp.table.table cimport table
 from cudf._lib.cpp.table.table_view cimport table_view
 from cudf._lib.cpp.types cimport size_type
 from cudf._lib.utils cimport (
+    columns_from_table_view,
     columns_from_unique_ptr,
     data_from_table_view,
     data_from_unique_ptr,
@@ -166,7 +167,7 @@ def copy_range(Column input_column,
 
 
 def gather(
-    columns: list,
+    list columns,
     Column gather_map,
     bool nullify=False
 ):
@@ -190,60 +191,80 @@ def gather(
     return columns_from_unique_ptr(move(c_result))
 
 
-def scatter(object source, Column scatter_map, Column target_column,
-            bool bounds_check=True):
-    """
-    Scattering input into target as per the scatter map,
-    input can be a list of scalars or can be a table
-    """
-
-    cdef column_view scatter_map_view = scatter_map.view()
-    cdef table_view target_table_view = table_view_from_columns(
-        (target_column,))
-    cdef bool c_bounds_check = bounds_check
+cdef scatter_scalar(list source_device_slrs,
+                    column_view scatter_map,
+                    table_view target_table,
+                    bool bounds_check):
+    cdef vector[reference_wrapper[constscalar]] c_source
+    cdef DeviceScalar d_slr
     cdef unique_ptr[table] c_result
 
-    # Needed for the table branch
-    cdef table_view source_table_view
+    c_source.reserve(len(source_device_slrs))
+    for d_slr in source_device_slrs:
+        c_source.push_back(
+            reference_wrapper[constscalar](d_slr.get_raw_ptr()[0])
+        )
+
+    with nogil:
+        c_result = move(
+            cpp_copying.scatter(
+                c_source,
+                scatter_map,
+                target_table,
+                bounds_check
+            )
+        )
 
-    # Needed for the scalar branch
-    cdef vector[reference_wrapper[constscalar]] source_scalars
-    cdef DeviceScalar slr
+    return columns_from_unique_ptr(move(c_result))
 
-    if isinstance(source, Column):
-        source_table_view = table_view_from_columns((<Column> source,))
 
-        with nogil:
-            c_result = move(
-                cpp_copying.scatter(
-                    source_table_view,
-                    scatter_map_view,
-                    target_table_view,
-                    c_bounds_check
-                )
-            )
-    else:
-        slr = as_device_scalar(source, target_column.dtype)
-        source_scalars.push_back(reference_wrapper[constscalar](
-            slr.get_raw_ptr()[0]))
+cdef scatter_column(list source_columns,
+                    column_view scatter_map,
+                    table_view target_table,
+                    bool bounds_check):
+    cdef table_view c_source = table_view_from_columns(source_columns)
+    cdef unique_ptr[table] c_result
 
-        with nogil:
-            c_result = move(
-                cpp_copying.scatter(
-                    source_scalars,
-                    scatter_map_view,
-                    target_table_view,
-                    c_bounds_check
-                )
+    with nogil:
+        c_result = move(
+            cpp_copying.scatter(
+                c_source,
+                scatter_map,
+                target_table,
+                bounds_check
             )
+        )
+    return columns_from_unique_ptr(move(c_result))
 
-    data, _ = data_from_unique_ptr(
-        move(c_result),
-        column_names=(None,),
-        index_names=None
-    )
 
-    return next(iter(data.values()))
+def scatter(list sources, Column scatter_map, list target_columns,
+            bool bounds_check=True):
+    """
+    Scattering source into target as per the scatter map.
+    `source` can be a list of scalars, or a list of columns. The number of
+    items in `sources` must equal the number of `target_columns` to scatter.
+    """
+    # TODO: Only single column scatter is used, we should explore multi-column
+    # scatter for frames for performance increase.
+
+    if len(sources) != len(target_columns):
+        raise ValueError("Mismatched number of source and target columns.")
+
+    if len(sources) == 0:
+        return []
+
+    cdef column_view scatter_map_view = scatter_map.view()
+    cdef table_view target_table_view = table_view_from_columns(target_columns)
+
+    if isinstance(sources[0], Column):
+        return scatter_column(
+            sources, scatter_map_view, target_table_view, bounds_check
+        )
+    else:
+        source_scalars = [as_device_scalar(slr) for slr in sources]
+        return scatter_scalar(
+            source_scalars, scatter_map_view, target_table_view, bounds_check
+        )
 
 
 def column_empty_like(Column input_column):
@@ -281,24 +302,14 @@ def column_allocate_like(Column input_column, size=None):
     return Column.from_unique_ptr(move(c_result))
 
 
-def table_empty_like(input_table, bool keep_index=True):
-
-    cdef table_view input_table_view = table_view_from_table(
-        input_table, not keep_index
-    )
-
+def columns_empty_like(list input_columns):
+    cdef table_view input_table_view = table_view_from_columns(input_columns)
     cdef unique_ptr[table] c_result
 
     with nogil:
         c_result = move(cpp_copying.empty_like(input_table_view))
 
-    return data_from_unique_ptr(
-        move(c_result),
-        column_names=input_table._column_names,
-        index_names=(
-            input_table._index._column_names if keep_index is True else None
-        )
-    )
+    return columns_from_unique_ptr(move(c_result))
 
 
 def column_slice(Column input_column, object indices):
@@ -330,40 +341,30 @@ def column_slice(Column input_column, object indices):
     return result
 
 
-def table_slice(input_table, object indices, bool keep_index=True):
-
-    cdef table_view input_table_view = table_view_from_table(
-        input_table, not keep_index
-    )
-
-    cdef vector[size_type] c_indices
-    c_indices.reserve(len(indices))
+def columns_slice(list input_columns, list indices):
+    """
+    Given a list of input columns, return columns sliced by ``indices``.
 
+    Returns a list of list of columns. The length of return is
+    `len(indices) / 2`. The `i`th item in return is a list of columns sliced
+    from ``input_columns`` with `slice(indices[i*2], indices[i*2 + 1])`.
+    """
+    cdef table_view input_table_view = table_view_from_columns(input_columns)
+    cdef vector[size_type] c_indices = indices
     cdef vector[table_view] c_result
 
-    cdef int index
-    for index in indices:
-        c_indices.push_back(index)
-
     with nogil:
         c_result = move(
             cpp_copying.slice(
                 input_table_view,
                 c_indices)
         )
 
-    num_of_result_cols = c_result.size()
     return [
-        data_from_table_view(
-            c_result[i],
-            input_table,
-            column_names=input_table._column_names,
-            index_names=(
-                input_table._index._column_names if (
-                    keep_index is True)
-                else None
-            )
-        ) for i in range(num_of_result_cols)]
+        columns_from_table_view(
+            c_result[i], input_columns
+        ) for i in range(c_result.size())
+    ]
 
 
 def column_split(Column input_column, object splits):
@@ -397,38 +398,24 @@ def column_split(Column input_column, object splits):
     return result
 
 
-def table_split(input_table, object splits, bool keep_index=True):
-
-    cdef table_view input_table_view = table_view_from_table(
-        input_table, not keep_index
-    )
-
-    cdef vector[size_type] c_splits
-    c_splits.reserve(len(splits))
+def columns_split(list input_columns, object splits):
 
+    cdef table_view input_table_view = table_view_from_columns(input_columns)
+    cdef vector[size_type] c_splits = splits
     cdef vector[table_view] c_result
 
-    cdef int split
-    for split in splits:
-        c_splits.push_back(split)
-
     with nogil:
         c_result = move(
             cpp_copying.split(
                 input_table_view,
                 c_splits)
         )
 
-    num_of_result_cols = c_result.size()
     return [
-        data_from_table_view(
-            c_result[i],
-            input_table,
-            column_names=input_table._column_names,
-            index_names=input_table._index_names if (
-                keep_index is True)
-            else None
-        ) for i in range(num_of_result_cols)]
+        columns_from_table_view(
+            c_result[i], input_columns
+        ) for i in range(c_result.size())
+    ]
 
 
 def _copy_if_else_column_column(Column lhs, Column rhs, Column boolean_mask):

python/cudf/cudf/_lib/stream_compaction.pyx

@@ -32,7 +32,7 @@ from cudf._lib.utils cimport (
 )
 
 
-def drop_nulls(columns: list, how="any", keys=None, thresh=None):
+def drop_nulls(list columns, how="any", keys=None, thresh=None):
     """
     Drops null rows from cols depending on key columns.
 
@@ -75,7 +75,7 @@ def drop_nulls(columns: list, how="any", keys=None, thresh=None):
     return columns_from_unique_ptr(move(c_result))
 
 
-def apply_boolean_mask(columns: list, Column boolean_mask):
+def apply_boolean_mask(list columns, Column boolean_mask):
     """
     Drops the rows which correspond to False in boolean_mask.
 
@@ -104,7 +104,7 @@ def apply_boolean_mask(columns: list, Column boolean_mask):
     return columns_from_unique_ptr(move(c_result))
 
 
-def drop_duplicates(columns: list,
+def drop_duplicates(list columns,
                     object keys=None,
                     object keep='first',
                     bool nulls_are_equal=True):

python/cudf/cudf/_lib/utils.pxd

@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2021, NVIDIA CORPORATION.
+# Copyright (c) 2020-2022, NVIDIA CORPORATION.
 
 from libcpp.memory cimport unique_ptr
 from libcpp.string cimport string
@@ -17,3 +17,4 @@ cdef data_from_table_view(
 cdef table_view table_view_from_columns(columns) except *
 cdef table_view table_view_from_table(tbl, ignore_index=*) except*
 cdef columns_from_unique_ptr(unique_ptr[table] c_tbl)
+cdef columns_from_table_view(table_view tv, object owners)

python/cudf/cudf/_lib/utils.pyx

@@ -311,6 +311,24 @@ cdef data_from_unique_ptr(
     }
     return data, index
 
+cdef columns_from_table_view(
+    table_view tv,
+    object owners,
+):
+    """
+    Given a ``cudf::table_view``, construsts a list of columns from it,
+    along with referencing an ``owner`` Python object that owns the memory
+    lifetime. ``owner`` must be either None or a list of column. If ``owner``
+    is a list of columns, the owner of the `i`th ``cudf::column_view`` in the
+    table view is ``owners[i]``. For more about memory ownership,
+    see ``Column.from_column_view``.
+    """
+
+    return [
+        Column.from_column_view(
+            tv.column(i), owners[i] if isinstance(owners, list) else None
+        ) for i in range(tv.num_columns())
+    ]
 
 cdef data_from_table_view(
     table_view tv,

python/cudf/cudf/core/_base_index.py

@@ -1522,6 +1522,9 @@ def _split_columns_by_levels(self, levels):
             [],
         )
 
+    def _split(self, splits):
+        raise NotImplementedError
+
     def sample(
         self,
         n=None,

python/cudf/cudf/core/column/column.py

@@ -593,9 +593,9 @@ def _scatter_by_column(
                     [value], [self], key
                 )[0]._with_type_metadata(self.dtype)
             else:
-                return libcudf.copying.scatter(
-                    value, key, self
-                )._with_type_metadata(self.dtype)
+                return libcudf.copying.scatter([value], key, [self])[
+                    0
+                ]._with_type_metadata(self.dtype)
         except RuntimeError as e:
             if "out of bounds" in str(e):
                 raise IndexError(