Add partition-wise Select support to cuDF-Polars

python/cudf_polars/cudf_polars/dsl/expressions/aggregation.py

@@ -40,6 +40,7 @@ def __init__(
         self.dtype = dtype
         self.name = name
         self.options = options
+        self.is_pointwise = False
         self.children = children
         if name not in Agg._SUPPORTED:
             raise NotImplementedError(

python/cudf_polars/cudf_polars/dsl/expressions/base.py

@@ -36,9 +36,11 @@ class ExecutionContext(IntEnum):
 class Expr(Node["Expr"]):
     """An abstract expression object."""
 
-    __slots__ = ("dtype",)
+    __slots__ = ("dtype", "is_pointwise")
     dtype: plc.DataType
     """Data type of the expression."""
+    is_pointwise: bool
+    """Whether this expression acts pointwise on its inputs."""
     # This annotation is needed because of https://github.com/python/mypy/issues/17981
     _non_child: ClassVar[tuple[str, ...]] = ("dtype",)
     """Names of non-child data (not Exprs) for reconstruction."""
@@ -164,6 +166,7 @@ def __init__(self, dtype: plc.DataType, error: str) -> None:
         self.dtype = dtype
         self.error = error
         self.children = ()
+        self.is_pointwise = True
 
 
 class NamedExpr:
@@ -243,6 +246,7 @@ class Col(Expr):
     def __init__(self, dtype: plc.DataType, name: str) -> None:
         self.dtype = dtype
         self.name = name
+        self.is_pointwise = True
         self.children = ()
 
     def do_evaluate(
@@ -280,6 +284,7 @@ def __init__(
         self.dtype = dtype
         self.index = index
         self.table_ref = table_ref
+        self.is_pointwise = True
         self.children = (column,)
 
     def do_evaluate(

python/cudf_polars/cudf_polars/dsl/expressions/binaryop.py

@@ -42,6 +42,7 @@ def __init__(
             op = BinOp._BOOL_KLEENE_MAPPING.get(op, op)
         self.op = op
         self.children = (left, right)
+        self.is_pointwise = True
         if not plc.binaryop.is_supported_operation(
             self.dtype, left.dtype, right.dtype, op
         ):

python/cudf_polars/cudf_polars/dsl/expressions/boolean.py

@@ -81,6 +81,14 @@ def __init__(
         self.options = options
         self.name = name
         self.children = children
+        self.is_pointwise = self.name not in (
+            BooleanFunction.Name.All,
+            BooleanFunction.Name.Any,
+            BooleanFunction.Name.IsDuplicated,
+            BooleanFunction.Name.IsFirstDistinct,
+            BooleanFunction.Name.IsLastDistinct,
+            BooleanFunction.Name.IsUnique,
+        )
         if self.name is BooleanFunction.Name.IsIn and not all(
             c.dtype == self.children[0].dtype for c in self.children
         ):

python/cudf_polars/cudf_polars/dsl/expressions/datetime.py

@@ -114,6 +114,7 @@ def __init__(
         self.options = options
         self.name = name
         self.children = children
+        self.is_pointwise = True
         if self.name not in self._COMPONENT_MAP:
             raise NotImplementedError(f"Temporal function {self.name}")
 

python/cudf_polars/cudf_polars/dsl/expressions/literal.py

@@ -38,6 +38,7 @@ def __init__(self, dtype: plc.DataType, value: pa.Scalar[Any]) -> None:
         assert value.type == plc.interop.to_arrow(dtype)
         self.value = value
         self.children = ()
+        self.is_pointwise = True
 
     def do_evaluate(
         self,
@@ -65,6 +66,7 @@ def __init__(self, dtype: plc.DataType, value: pl.Series) -> None:
         data = value.to_arrow()
         self.value = data.cast(dtypes.downcast_arrow_lists(data.type))
         self.children = ()
+        self.is_pointwise = True
 
     def get_hashable(self) -> Hashable:
         """Compute a hash of the column."""

python/cudf_polars/cudf_polars/dsl/expressions/rolling.py

@@ -24,6 +24,7 @@ def __init__(self, dtype: plc.DataType, options: Any, agg: Expr) -> None:
         self.dtype = dtype
         self.options = options
         self.children = (agg,)
+        self.is_pointwise = False
         raise NotImplementedError("Rolling window not implemented")
 
 
@@ -35,4 +36,5 @@ def __init__(self, dtype: plc.DataType, options: Any, agg: Expr, *by: Expr) -> N
         self.dtype = dtype
         self.options = options
         self.children = (agg, *by)
+        self.is_pointwise = False
         raise NotImplementedError("Grouped rolling window not implemented")

python/cudf_polars/cudf_polars/dsl/expressions/selection.py

@@ -30,6 +30,7 @@ class Gather(Expr):
     def __init__(self, dtype: plc.DataType, values: Expr, indices: Expr) -> None:
         self.dtype = dtype
         self.children = (values, indices)
+        self.is_pointwise = False
 
     def do_evaluate(
         self,
@@ -71,6 +72,7 @@ class Filter(Expr):
     def __init__(self, dtype: plc.DataType, values: Expr, indices: Expr):
         self.dtype = dtype
         self.children = (values, indices)
+        self.is_pointwise = True
 
     def do_evaluate(
         self,

python/cudf_polars/cudf_polars/dsl/expressions/sorting.py

@@ -32,6 +32,7 @@ def __init__(
         self.dtype = dtype
         self.options = options
         self.children = (column,)
+        self.is_pointwise = False
 
     def do_evaluate(
         self,
@@ -71,6 +72,7 @@ def __init__(
         self.dtype = dtype
         self.options = options
         self.children = (column, *by)
+        self.is_pointwise = False
 
     def do_evaluate(
         self,

python/cudf_polars/cudf_polars/dsl/expressions/string.py

@@ -106,6 +106,7 @@ def __init__(
         self.options = options
         self.name = name
         self.children = children
+        self.is_pointwise = True
         self._validate_input()
 
     def _validate_input(self):

python/cudf_polars/cudf_polars/dsl/expressions/ternary.py

@@ -34,6 +34,7 @@ def __init__(
     ) -> None:
         self.dtype = dtype
         self.children = (when, then, otherwise)
+        self.is_pointwise = True
 
     def do_evaluate(
         self,

python/cudf_polars/cudf_polars/dsl/expressions/unary.py

@@ -33,6 +33,7 @@ class Cast(Expr):
     def __init__(self, dtype: plc.DataType, value: Expr) -> None:
         self.dtype = dtype
         self.children = (value,)
+        self.is_pointwise = True
         if not dtypes.can_cast(value.dtype, self.dtype):
             raise NotImplementedError(
                 f"Can't cast {value.dtype.id().name} to {self.dtype.id().name}"
@@ -63,6 +64,7 @@ class Len(Expr):
     def __init__(self, dtype: plc.DataType) -> None:
         self.dtype = dtype
         self.children = ()
+        self.is_pointwise = False
 
     def do_evaluate(
         self,
@@ -147,6 +149,14 @@ def __init__(
         self.name = name
         self.options = options
         self.children = children
+        self.is_pointwise = self.name not in (
+            "cum_min",
+            "cum_max",
+            "cum_prod",
+            "cum_sum",
+            "set_sorted",
+            "unique",
+        )
 
         if self.name not in UnaryFunction._supported_fns:
             raise NotImplementedError(f"Unary function {name=}")

python/cudf_polars/cudf_polars/dsl/traversal.py

@@ -10,7 +10,7 @@
 from cudf_polars.typing import U_contra, V_co
 
 if TYPE_CHECKING:
-    from collections.abc import Callable, Generator, Mapping, MutableMapping
+    from collections.abc import Callable, Generator, Mapping, MutableMapping, Sequence
 
     from cudf_polars.typing import GenericTransformer, NodeT
 
@@ -23,22 +23,22 @@
 ]
 
 
-def traversal(node: NodeT) -> Generator[NodeT, None, None]:
+def traversal(nodes: Sequence[NodeT]) -> Generator[NodeT, None, None]:
     """
     Pre-order traversal of nodes in an expression.
 
     Parameters
     ----------
-    node
-        Root of expression to traverse.
+    nodes
+        Roots of expressions to traverse.
 
     Yields
     ------
-    Unique nodes in the expression, parent before child, children
+    Unique nodes in the expressions, parent before child, children
     in-order from left to right.
     """
-    seen = {node}
-    lifo = [node]
+    seen = set(nodes)
+    lifo = list(nodes)
 
     while lifo:
         node = lifo.pop()

python/cudf_polars/cudf_polars/experimental/parallel.py

@@ -9,8 +9,9 @@
 from functools import reduce
 from typing import TYPE_CHECKING, Any
 
-import cudf_polars.experimental.io  # noqa: F401
-from cudf_polars.dsl.ir import IR, Cache, Projection, Union
+import cudf_polars.experimental.io
+import cudf_polars.experimental.select  # noqa: F401
+from cudf_polars.dsl.ir import IR, Cache, Filter, HStack, Projection, Select, Union
 from cudf_polars.dsl.traversal import CachingVisitor, traversal
 from cudf_polars.experimental.base import PartitionInfo, _concat, get_key_name
 from cudf_polars.experimental.dispatch import (
@@ -112,7 +113,7 @@ def task_graph(
     """
     graph = reduce(
         operator.or_,
-        (generate_ir_tasks(node, partition_info) for node in traversal(ir)),
+        (generate_ir_tasks(node, partition_info) for node in traversal([ir])),
     )
 
     key_name = get_key_name(ir)
@@ -226,6 +227,8 @@ def _lower_ir_pwise(
 
 lower_ir_node.register(Projection, _lower_ir_pwise)
 lower_ir_node.register(Cache, _lower_ir_pwise)
+lower_ir_node.register(Filter, _lower_ir_pwise)
+lower_ir_node.register(HStack, _lower_ir_pwise)
 
 
 def _generate_ir_tasks_pwise(
@@ -245,3 +248,6 @@ def _generate_ir_tasks_pwise(
 
 generate_ir_tasks.register(Projection, _generate_ir_tasks_pwise)
 generate_ir_tasks.register(Cache, _generate_ir_tasks_pwise)
+generate_ir_tasks.register(Filter, _generate_ir_tasks_pwise)
+generate_ir_tasks.register(HStack, _generate_ir_tasks_pwise)
+generate_ir_tasks.register(Select, _generate_ir_tasks_pwise)

python/cudf_polars/cudf_polars/experimental/select.py

@@ -0,0 +1,36 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-License-Identifier: Apache-2.0
+"""Parallel Select Logic."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from cudf_polars.dsl.ir import Select
+from cudf_polars.dsl.traversal import traversal
+from cudf_polars.experimental.dispatch import lower_ir_node
+
+if TYPE_CHECKING:
+    from collections.abc import MutableMapping
+
+    from cudf_polars.dsl.ir import IR
+    from cudf_polars.experimental.base import PartitionInfo
+    from cudf_polars.experimental.parallel import LowerIRTransformer
+
+
+@lower_ir_node.register(Select)
+def _(
+    ir: Select, rec: LowerIRTransformer
+) -> tuple[IR, MutableMapping[IR, PartitionInfo]]:
+    child, partition_info = rec(ir.children[0])
+    pi = partition_info[child]
+    if pi.count > 1 and not all(
+        expr.is_pointwise for expr in traversal([e.value for e in ir.exprs])
+    ):
+        # TODO: Handle non-pointwise expressions.
+        raise NotImplementedError(
+            f"Selection {ir} does not support multiple partitions."
+        )
+    new_node = ir.reconstruct([child])
+    partition_info[new_node] = pi
+    return new_node, partition_info

python/cudf_polars/tests/dsl/test_traversal.py

@@ -32,21 +32,21 @@ def test_traversal_unique():
     dt = plc.DataType(plc.TypeId.INT8)
 
     e1 = make_expr(dt, "a", "a")
-    unique_exprs = list(traversal(e1))
+    unique_exprs = list(traversal([e1]))
 
     assert len(unique_exprs) == 2
     assert set(unique_exprs) == {expr.Col(dt, "a"), e1}
     assert unique_exprs == [e1, expr.Col(dt, "a")]
 
     e2 = make_expr(dt, "a", "b")
-    unique_exprs = list(traversal(e2))
+    unique_exprs = list(traversal([e2]))
 
     assert len(unique_exprs) == 3
     assert set(unique_exprs) == {expr.Col(dt, "a"), expr.Col(dt, "b"), e2}
     assert unique_exprs == [e2, expr.Col(dt, "a"), expr.Col(dt, "b")]
 
     e3 = make_expr(dt, "b", "a")
-    unique_exprs = list(traversal(e3))
+    unique_exprs = list(traversal([e3]))
 
     assert len(unique_exprs) == 3
     assert set(unique_exprs) == {expr.Col(dt, "a"), expr.Col(dt, "b"), e3}

python/cudf_polars/tests/experimental/test_select.py

@@ -0,0 +1,54 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import pytest
+
+import polars as pl
+
+from cudf_polars.testing.asserts import assert_gpu_result_equal
+
+
+@pytest.fixture(scope="module")
+def engine():
+    return pl.GPUEngine(
+        raise_on_fail=True,
+        executor="dask-experimental",
+        executor_options={"max_rows_per_partition": 3},
+    )
+
+
+@pytest.fixture(scope="module")
+def df():
+    return pl.LazyFrame(
+        {
+            "a": [1, 2, 3, 4, 5, 6, 7],
+            "b": [1, 1, 1, 1, 1, 1, 1],
+        }
+    )
+
+
+def test_select(df, engine):
+    query = df.select(
+        pl.col("a") + pl.col("b"), (pl.col("a") * 2 + pl.col("b")).alias("d")
+    )
+    assert_gpu_result_equal(query, engine=engine)
+
+
+def test_select_reduce_raises(df, engine):
+    query = df.select(
+        (pl.col("a") + pl.col("b")).max(),
+        (pl.col("a") * 2 + pl.col("b")).alias("d").mean(),
+    )
+    with pytest.raises(
+        pl.exceptions.ComputeError,
+        match="NotImplementedError",
+    ):
+        assert_gpu_result_equal(query, engine=engine)
+
+
+def test_select_with_cse_no_agg(df, engine):
+    expr = pl.col("a") + pl.col("a")
+    query = df.select(expr, (expr * 2).alias("b"), ((expr * 2) + 10).alias("c"))
+    assert_gpu_result_equal(query, engine=engine)