Add fast paths for DataFrame.to_cupy

python/cudf/benchmarks/API/bench_dataframe.py

@@ -349,3 +349,9 @@ def bench_nsmallest(benchmark, dataframe, num_cols_to_sort, n):
 )
 def bench_where(benchmark, dataframe, cond, other):
     benchmark(dataframe.where, cond, other)
+
+
+@benchmark_with_object(cls="dataframe", dtype="float", nulls=False, cols=20)
+@pytest.mark.pandas_incompatible
+def bench_to_cupy(benchmark, dataframe):
+    benchmark(dataframe.to_cupy)

python/cudf/benchmarks/common/config.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
+# Copyright (c) 2022-2025, NVIDIA CORPORATION.
 
 """Module used for global configuration of benchmarks.
 
@@ -64,7 +64,7 @@ def pytest_sessionfinish(session, exitstatus):
 # Constants used to define benchmarking standards.
 if "CUDF_BENCHMARKS_DEBUG_ONLY" in os.environ:
     NUM_ROWS = [10, 20]
-    NUM_COLS = [1, 6]
+    NUM_COLS = [1, 6, 20]
 else:
     NUM_ROWS = [100, 10_000, 1_000_000]
-    NUM_COLS = [1, 6]
+    NUM_COLS = [1, 6, 20]

python/cudf/cudf/core/frame.py

@@ -8,6 +8,7 @@
 from typing import TYPE_CHECKING, Any, Literal
 
 import cupy
+import cupy as cp
 import numpy
 import numpy as np
 import pyarrow as pa
@@ -524,6 +525,15 @@ def to_array(
                 matrix[:, i] = to_array(col, dtype)
             return matrix
 
+    @_performance_tracking
+    def to_pylibcudf(self) -> tuple[plc.Table, dict[str, Any]]:
+        """
+        Converts Frame to a pylibcudf.Table.
+        Note: This method should not be called directly on a Frame object
+        Instead, it should be called on subclasses like DataFrame/Series.
+        """
+        raise NotImplementedError(f"{type(self)} must implement to_pylibcudf")
+
     @_performance_tracking
     def to_cupy(
         self,
@@ -550,6 +560,51 @@ def to_cupy(
         -------
         cupy.ndarray
         """
+        if (
+            self._num_columns > 1
+            and na_value is None
+            and self._columns[0].dtype.kind in {"i", "u", "f", "b"}
+            and all(
+                not col.nullable and col.dtype == self._columns[0].dtype
+                for col in self._columns
+            )
+        ):
+            if dtype is None:
+                dtype = np.dtype(self._columns[0].dtype)
+
+            shape = (len(self), self._num_columns)
+            out = cupy.empty(shape, dtype=dtype, order="F")
+
+            table = self.to_pylibcudf()[0]
+            if isinstance(table, plc.Column):
+                table = plc.Table([table])
+            plc.reshape.table_to_array(
+                table,
+                out.data.ptr,
+                out.nbytes,
+            )
+            return out
+        elif self._num_columns == 1:
+            col = self._columns[0]
+            final_dtype = col.dtype if dtype is None else dtype
+
+            if (
+                not copy
+                and col.dtype.kind in {"i", "u", "f", "b"}
+                and cp.can_cast(col.dtype, final_dtype)
+            ):
+                if col.has_nulls():
+                    if na_value is not None:
+                        col = col.fillna(na_value)
+                    else:
+                        return self._to_array(
+                            lambda col: col.values,
+                            cupy,
+                            copy,
+                            dtype,
+                            na_value,
+                        )
+                return cp.asarray(col, dtype=final_dtype).reshape((-1, 1))
         return self._to_array(
             lambda col: col.values,
             cupy,

python/cudf/cudf/core/single_column_frame.py

@@ -147,7 +147,6 @@ def to_cupy(
             not copy
             and col.dtype.kind in {"i", "u", "f", "b"}
             and cp.can_cast(col.dtype, final_dtype)
-            and not col.has_nulls()
         ):
             if col.has_nulls():
                 if na_value is not None:

python/cudf/cudf/tests/test_dataframe.py

@@ -1270,6 +1270,34 @@ def test_dataframe_to_cupy():
         np.testing.assert_array_equal(df[k].to_numpy(), mat[:, i])
 
 
+@pytest.mark.parametrize("has_nulls", [False, True])
+@pytest.mark.parametrize("use_na_value", [False, True])
+def test_dataframe_to_cupy_single_column(has_nulls, use_na_value):
+    nelem = 10
+    data = np.arange(nelem, dtype=np.float64)
+
+    if has_nulls:
+        data = data.astype("object")
+        data[::2] = None
+
+    df = cudf.DataFrame({"a": data})
+
+    if has_nulls and not use_na_value:
+        with pytest.raises(ValueError, match="Column must have no nulls"):
+            df.to_cupy()
+        return
+
+    na_value = 0.0 if use_na_value else None
+    expected = (
+        cupy.asarray(df["a"].fillna(na_value))
+        if has_nulls
+        else cupy.asarray(df["a"])
+    )
+    result = df.to_cupy(na_value=na_value)
+    assert result.shape == (nelem, 1)
+    assert_eq(result.ravel(), expected)
+
+
 def test_dataframe_to_cupy_null_values():
     df = cudf.DataFrame()
 

python/cudf/cudf/tests/test_series.py

@@ -3091,6 +3091,7 @@ def test_series_to_cupy(dtype, has_nulls, use_na_value):
 
     if not has_nulls:
         assert_eq(sr.values, cp.asarray(sr))
+        return
 
     if has_nulls and not use_na_value:
         with pytest.raises(ValueError, match="Column must have no nulls"):

python/pylibcudf/pylibcudf/libcudf/reshape.pxd

@@ -1,10 +1,17 @@
-# Copyright (c) 2019-2024, NVIDIA CORPORATION.
+# Copyright (c) 2019-2025, NVIDIA CORPORATION.
 from libcpp.memory cimport unique_ptr
 from pylibcudf.exception_handler cimport libcudf_exception_handler
 from pylibcudf.libcudf.column.column cimport column
 from pylibcudf.libcudf.table.table cimport table
 from pylibcudf.libcudf.table.table_view cimport table_view
-from pylibcudf.libcudf.types cimport size_type
+from pylibcudf.libcudf.types cimport size_type, data_type
+from pylibcudf.libcudf.utilities.span cimport device_span
+
+from rmm.librmm.cuda_stream_view cimport cuda_stream_view
+
+cdef extern from "cuda/functional" namespace "cuda::std":
+    cdef cppclass byte:
+        pass
 
 
 cdef extern from "cudf/reshape.hpp" namespace "cudf" nogil:
@@ -14,3 +21,8 @@ cdef extern from "cudf/reshape.hpp" namespace "cudf" nogil:
     cdef unique_ptr[table] tile(
         table_view source_table, size_type count
     ) except +libcudf_exception_handler
+    cdef void table_to_array(
+        table_view input_table,
+        device_span[byte] output,
+        cuda_stream_view stream
+    ) except +libcudf_exception_handler

python/pylibcudf/pylibcudf/reshape.pxd

@@ -1,11 +1,23 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
+
+from libc.stdint cimport uintptr_t
 
 from pylibcudf.libcudf.types cimport size_type
 
+from rmm.pylibrmm.stream cimport Stream
+from rmm.pylibrmm.device_buffer cimport DeviceBuffer
+
 from .column cimport Column
 from .scalar cimport Scalar
 from .table cimport Table
+from .types cimport DataType
 
 
 cpdef Column interleave_columns(Table source_table)
 cpdef Table tile(Table source_table, size_type count)
+cpdef void table_to_array(
+    Table input_table,
+    uintptr_t ptr,
+    size_type size,
+    Stream stream=*
+)

python/pylibcudf/pylibcudf/reshape.pyi

@@ -1,7 +1,15 @@
 # Copyright (c) 2024, NVIDIA CORPORATION.
 
+from rmm.pylibrmm.stream import Stream
+
 from pylibcudf.column import Column
 from pylibcudf.table import Table
 
 def interleave_columns(source_table: Table) -> Column: ...
 def tile(source_table: Table, count: int) -> Table: ...
+def table_to_array(
+    input_table: Table,
+    ptr: int,
+    size: int,
+    stream: Stream,
+) -> None: ...

python/pylibcudf/pylibcudf/reshape.pyx

@@ -1,19 +1,28 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 
+from libc.stdint cimport uintptr_t
 from libcpp.memory cimport unique_ptr
 from libcpp.utility cimport move
+from libcpp.limits cimport numeric_limits
 from pylibcudf.libcudf.column.column cimport column
 from pylibcudf.libcudf.reshape cimport (
     interleave_columns as cpp_interleave_columns,
     tile as cpp_tile,
+    table_to_array as cpp_table_to_array,
+    byte,
 )
 from pylibcudf.libcudf.table.table cimport table
 from pylibcudf.libcudf.types cimport size_type
 
+from pylibcudf.libcudf.utilities.span cimport device_span
+
+from rmm.pylibrmm.stream cimport Stream
+
 from .column cimport Column
 from .table cimport Table
+from .utils cimport _get_stream
 
-__all__ = ["interleave_columns", "tile"]
+__all__ = ["interleave_columns", "tile", "table_to_array"]
 
 cpdef Column interleave_columns(Table source_table):
     """Interleave columns of a table into a single column.
@@ -67,3 +76,41 @@ cpdef Table tile(Table source_table, size_type count):
         c_result = cpp_tile(source_table.view(), count)
 
     return Table.from_libcudf(move(c_result))
+
+
+cpdef void table_to_array(
+    Table input_table,
+    uintptr_t ptr,
+    size_type size,
+    Stream stream=None
+):
+    """
+    Copy a table into a preallocated column-major device array.
+    Parameters
+    ----------
+    input_table : Table
+        A table with fixed-width, non-nullable columns of the same type.
+    ptr : uintptr_t
+        A device pointer to the beginning of the output buffer.
+    size : size_type
+        The total number of bytes available at `ptr`.
+        Must be at least `num_rows * num_columns * sizeof(dtype)`.
+    stream : Stream | None
+        CUDA stream on which to perform the operation.
+    """
+    if size > numeric_limits[size_type].max():
+        raise ValueError(
+            "Size exceeds the int32_t limit."
+        )
+    stream = _get_stream(stream)
+
+    cdef device_span[byte] span = device_span[byte](
+        <byte*> ptr, size
+    )
+
+    with nogil:
+        cpp_table_to_array(
+            input_table.view(),
+            span,
+            stream.view()
+        )

python/pylibcudf/pylibcudf/tests/test_reshape.py

@@ -5,6 +5,17 @@
 from utils import assert_column_eq, assert_table_eq
 
 import pylibcudf as plc
+from pylibcudf.types import TypeId
+
+
+@pytest.fixture(scope="module")
+def np():
+    return pytest.importorskip("cupy")
+
+
+@pytest.fixture(scope="module")
+def cp():
+    return pytest.importorskip("cupy")
 
 
 @pytest.fixture(scope="module")
@@ -37,3 +48,34 @@ def test_tile(reshape_data, cnt):
     )
 
     assert_table_eq(expect, got)
+
+
+@pytest.mark.parametrize(
+    "dtype, type_id",
+    [
+        ("int32", TypeId.INT32),
+        ("int64", TypeId.INT64),
+        ("float32", TypeId.FLOAT32),
+        ("float64", TypeId.FLOAT64),
+    ],
+)
+def test_table_to_array(dtype, type_id, np, cp):
+    arrow_type = pa.from_numpy_dtype(getattr(np, dtype))
+    arrs = [
+        pa.array([1, 2, 3], type=arrow_type),
+        pa.array([4, 5, 6], type=arrow_type),
+    ]
+    arrow_tbl = pa.Table.from_arrays(arrs, names=["a", "b"])
+    tbl = plc.interop.from_arrow(arrow_tbl)
+
+    rows, cols = tbl.num_rows(), tbl.num_columns()
+    got = cp.empty((rows, cols), dtype=dtype, order="F")
+
+    plc.reshape.table_to_array(
+        tbl,
+        got.data.ptr,
+        got.nbytes,
+    )
+
+    expect = cp.array([[1, 4], [2, 5], [3, 6]], dtype=dtype)
+    cp.testing.assert_array_equal(expect, got)