pylibcudf.Column: add device_buffer_size and register a dask.sizeof function for cudf-polars Column and DataFrame

docs/cudf/source/conf.py

@@ -405,6 +405,7 @@ def _generate_namespaces(namespaces):
     "type_id",
     # Unknown base types
     "int32_t",
+    "uint64_t",
     "void",
 }
 

python/cudf_polars/cudf_polars/experimental/dask_serialize.py → python/cudf_polars/cudf_polars/experimental/dask_registers.py

@@ -1,12 +1,13 @@
 # SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES.
 # SPDX-License-Identifier: Apache-2.0
 
-"""Dask serialization."""
+"""Dask function registrations such as serializers and dispatch implementations."""
 
 from __future__ import annotations
 
 from typing import TYPE_CHECKING, ClassVar, overload
 
+from dask.sizeof import sizeof as sizeof_dispatch
 from distributed.protocol import dask_deserialize, dask_serialize
 from distributed.protocol.cuda import cuda_deserialize, cuda_serialize
 from distributed.utils import log_errors
@@ -21,35 +22,35 @@
 
     from cudf_polars.typing import ColumnHeader, DataFrameHeader
 
-__all__ = ["SerializerManager", "register"]
+__all__ = ["DaskRegisterManager", "register"]
 
 
-class SerializerManager:  # pragma: no cover; Only used with Distributed scheduler
+class DaskRegisterManager:  # pragma: no cover; Only used with Distributed scheduler
     """Manager to ensure ensure serializer is only registered once."""
 
-    _serializer_registered: bool = False
+    _registered: bool = False
     _client_run_executed: ClassVar[set[str]] = set()
 
     @classmethod
-    def register_serialize(cls) -> None:
+    def register_once(cls) -> None:
         """Register Dask/cudf-polars serializers in calling process."""
-        if not cls._serializer_registered:
-            from cudf_polars.experimental.dask_serialize import register
+        if not cls._registered:
+            from cudf_polars.experimental.dask_registers import register
 
             register()
-            cls._serializer_registered = True
+            cls._registered = True
 
     @classmethod
     def run_on_cluster(cls, client: Client) -> None:
-        """Run serializer registration on the workers and scheduler."""
+        """Run register on the workers and scheduler once."""
         if client.id not in cls._client_run_executed:
-            client.run(cls.register_serialize)
-            client.run_on_scheduler(cls.register_serialize)
+            client.run(cls.register_once)
+            client.run_on_scheduler(cls.register_once)
             cls._client_run_executed.add(client.id)
 
 
 def register() -> None:
-    """Register dask serialization routines for DataFrames."""
+    """Register dask serialization and dispatch functions."""
 
     @overload
     def serialize_column_or_frame(
@@ -128,3 +129,13 @@ def _(header: ColumnHeader, frames: tuple[memoryview, memoryview]) -> Column:
             # Copy the second frame (the gpudata in host memory) back to the gpu
             frames = frames[0], plc.gpumemoryview(rmm.DeviceBuffer.to_device(frames[1]))
             return Column.deserialize(header, frames)
+
+    @sizeof_dispatch.register(Column)
+    def _(x: Column) -> int:
+        """The total size of the device buffers used by the DataFrame or Column."""
+        return x.obj.device_buffer_size()
+
+    @sizeof_dispatch.register(DataFrame)
+    def _(x: DataFrame) -> int:
+        """The total size of the device buffers used by the DataFrame or Column."""
+        return sum(c.obj.device_buffer_size() for c in x.columns)

python/cudf_polars/cudf_polars/experimental/parallel.py

@@ -145,11 +145,11 @@ def get_scheduler(config_options: ConfigOptions) -> Any:
     ):  # pragma: no cover; block depends on executor type and Distributed cluster
         from distributed import get_client
 
-        from cudf_polars.experimental.dask_serialize import SerializerManager
+        from cudf_polars.experimental.dask_registers import DaskRegisterManager
 
         client = get_client()
-        SerializerManager.register_serialize()
-        SerializerManager.run_on_cluster(client)
+        DaskRegisterManager.register_once()
+        DaskRegisterManager.run_on_cluster(client)
         return client.get
     elif scheduler == "synchronous":
         from cudf_polars.experimental.scheduler import synchronous_scheduler

python/cudf_polars/tests/experimental/test_dask_serialize.py

@@ -13,7 +13,7 @@
 import rmm
 
 from cudf_polars.containers import DataFrame
-from cudf_polars.experimental.dask_serialize import register
+from cudf_polars.experimental.dask_registers import register
 
 # Must register serializers before running tests
 register()

python/cudf_polars/tests/experimental/test_dask_sizeof.py

@@ -0,0 +1,33 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION & AFFILIATES.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import pyarrow as pa
+import pytest
+from dask.sizeof import sizeof
+
+import pylibcudf as plc
+
+from cudf_polars.containers import DataFrame
+from cudf_polars.experimental.dask_registers import register
+
+# Must register sizeof dispatch before running tests
+register()
+
+
+@pytest.mark.parametrize(
+    "arrow_tbl, size",
+    [
+        (pa.table([]), 0),
+        (pa.table({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}), 9 * 8),
+        (pa.table({"a": [1, 2, 3]}), 3 * 8),
+        (pa.table({"a": ["a"], "b": ["bc"]}), 2 * 8 + 3),
+        (pa.table({"a": [1, 2, None]}), 88),
+    ],
+)
+def test_dask_sizeof(arrow_tbl, size):
+    plc_tbl = plc.interop.from_arrow(arrow_tbl)
+    df = DataFrame.from_table(plc_tbl, names=arrow_tbl.column_names)
+    assert sizeof(df) == size
+    assert sum(sizeof(c) for c in df.columns) == size

python/pylibcudf/pylibcudf/column.pxd

@@ -2,6 +2,8 @@
 
 from libcpp.memory cimport unique_ptr
 from libcpp.vector cimport vector
+from libc.stdint cimport uint64_t
+
 from rmm.librmm.device_buffer cimport device_buffer
 from rmm.pylibrmm.stream cimport Stream
 from pylibcudf.libcudf.column.column cimport column
@@ -68,6 +70,7 @@ cdef class Column:
     cpdef gpumemoryview null_mask(self)
     cpdef list children(self)
     cpdef Column copy(self)
+    cpdef uint64_t device_buffer_size(self)
     cpdef Column with_mask(self, gpumemoryview, size_type)
 
     cpdef ListColumnView list_view(self)

python/pylibcudf/pylibcudf/column.pyi

@@ -50,6 +50,7 @@ class Column:
     def null_mask(self) -> gpumemoryview | None: ...
     def children(self) -> list[Column]: ...
     def copy(self) -> Column: ...
+    def device_buffer_size(self) -> int: ...
     def with_mask(
         self, mask: gpumemoryview | None, null_count: int
     ) -> Column: ...

python/pylibcudf/pylibcudf/column.pyx

@@ -51,11 +51,11 @@ from ._interop_helpers cimport (
 from .null_mask cimport bitmask_allocation_size_bytes
 from .utils cimport _get_stream
 
+from .gpumemoryview import _datatype_from_dtype_desc
 from ._interop_helpers import ColumnMetadata
 
 import functools
 
-
 __all__ = ["Column", "ListColumnView", "is_c_contiguous"]
 
 
@@ -767,6 +767,30 @@ cdef class Column:
             c_result = make_unique[column](self.view())
         return Column.from_libcudf(move(c_result))
 
+    cpdef uint64_t device_buffer_size(self):
+        """
+        The total size of the device buffers used by the Column.
+
+        Notes
+        -----
+        Since Columns rely on Python memoryview-like semantics to maintain
+        shared ownership of the data, the device buffers underlying this column
+        might be shared between other data structures including other columns.
+
+        Returns
+        -------
+        Number of bytes.
+        """
+        cdef uint64_t ret = 0
+        if self.data() is not None:
+            ret += self.data().nbytes
+        if self.null_mask() is not None:
+            ret += self.null_mask().nbytes
+        if self.children() is not None:
+            for child in self.children():
+                ret += (<Column?>child).device_buffer_size()
+        return ret
+
     def _create_nested_column_metadata(self):
         return ColumnMetadata(
             children_meta=[
@@ -855,34 +879,6 @@ cdef class ListColumnView:
         return lists_column_view(self._column.view())
 
 
-@functools.cache
-def _datatype_from_dtype_desc(desc):
-    mapping = {
-        'u1': type_id.UINT8,
-        'u2': type_id.UINT16,
-        'u4': type_id.UINT32,
-        'u8': type_id.UINT64,
-        'i1': type_id.INT8,
-        'i2': type_id.INT16,
-        'i4': type_id.INT32,
-        'i8': type_id.INT64,
-        'f4': type_id.FLOAT32,
-        'f8': type_id.FLOAT64,
-        'b1': type_id.BOOL8,
-        'M8[s]': type_id.TIMESTAMP_SECONDS,
-        'M8[ms]': type_id.TIMESTAMP_MILLISECONDS,
-        'M8[us]': type_id.TIMESTAMP_MICROSECONDS,
-        'M8[ns]': type_id.TIMESTAMP_NANOSECONDS,
-        'm8[s]': type_id.DURATION_SECONDS,
-        'm8[ms]': type_id.DURATION_MILLISECONDS,
-        'm8[us]': type_id.DURATION_MICROSECONDS,
-        'm8[ns]': type_id.DURATION_NANOSECONDS,
-    }
-    if desc not in mapping:
-        raise ValueError(f"Unsupported dtype: {desc}")
-    return DataType(mapping[desc])
-
-
 def is_c_contiguous(
     shape: Sequence[int], strides: None | Sequence[int], itemsize: int
 ) -> bool:

python/pylibcudf/pylibcudf/gpumemoryview.pxd

@@ -1,9 +1,10 @@
 # Copyright (c) 2023-2025, NVIDIA CORPORATION.
-from libc.stdint cimport uintptr_t
+from libc.stdint cimport uint64_t, uintptr_t
 
 cdef class gpumemoryview:
     # TODO: Eventually probably want to make this opaque, but for now it's fine
     # to treat this object as something like a POD struct
     cdef readonly uintptr_t ptr
     cdef readonly object obj
     cdef readonly dict cai
+    cdef readonly uint64_t nbytes

python/pylibcudf/pylibcudf/gpumemoryview.pyx

@@ -3,8 +3,42 @@
 import functools
 import operator
 
+from .types cimport DataType, size_of, type_id
+
+from pylibcudf.libcudf.types cimport size_type
+
+
 __all__ = ["gpumemoryview"]
 
+
+@functools.cache
+def _datatype_from_dtype_desc(desc):
+    mapping = {
+        'u1': type_id.UINT8,
+        'u2': type_id.UINT16,
+        'u4': type_id.UINT32,
+        'u8': type_id.UINT64,
+        'i1': type_id.INT8,
+        'i2': type_id.INT16,
+        'i4': type_id.INT32,
+        'i8': type_id.INT64,
+        'f4': type_id.FLOAT32,
+        'f8': type_id.FLOAT64,
+        'b1': type_id.BOOL8,
+        'M8[s]': type_id.TIMESTAMP_SECONDS,
+        'M8[ms]': type_id.TIMESTAMP_MILLISECONDS,
+        'M8[us]': type_id.TIMESTAMP_MICROSECONDS,
+        'M8[ns]': type_id.TIMESTAMP_NANOSECONDS,
+        'm8[s]': type_id.DURATION_SECONDS,
+        'm8[ms]': type_id.DURATION_MILLISECONDS,
+        'm8[us]': type_id.DURATION_MICROSECONDS,
+        'm8[ns]': type_id.DURATION_NANOSECONDS,
+    }
+    if desc not in mapping:
+        raise ValueError(f"Unsupported dtype: {desc}")
+    return DataType(mapping[desc])
+
+
 cdef class gpumemoryview:
     """Minimal representation of a memory buffer.
 
@@ -27,23 +61,19 @@ cdef class gpumemoryview:
         # TODO: Need to respect readonly
         self.ptr = cai["data"][0]
 
+        # Compute the buffer size.
+        cdef size_type itemsize = size_of(
+            _datatype_from_dtype_desc(
+                cai["typestr"][1:]  # ignore the byteorder (the first char).
+            )
+        )
+        self.nbytes = functools.reduce(operator.mul, cai["shape"]) * itemsize
+
     @property
     def __cuda_array_interface__(self):
         return self.cai
 
     def __len__(self):
         return self.obj.__cuda_array_interface__["shape"][0]
 
-    @property
-    def nbytes(self):
-        cai = self.obj.__cuda_array_interface__
-        shape, typestr = cai["shape"], cai["typestr"]
-
-        # Get element size from typestr, format is two character specifying
-        # the type and the latter part is the number of bytes. E.g., '<f4' for
-        # 32-bit (4-byte) float.
-        element_size = int(typestr[2:])
-
-        return functools.reduce(operator.mul, shape) * element_size
-
     __hash__ = None