[FEAT] Enable init args for stateful UDFs

daft/daft/__init__.pyi

@@ -9,7 +9,7 @@ from daft.io.scan import ScanOperator
 from daft.plan_scheduler.physical_plan_scheduler import PartitionT
 from daft.runners.partitioning import PartitionCacheEntry
 from daft.sql.sql_connection import SQLConnection
-from daft.udf import PartialStatefulUDF, PartialStatelessUDF
+from daft.udf import InitArgsType, PartialStatefulUDF, PartialStatelessUDF
 
 if TYPE_CHECKING:
     import pyarrow as pa
@@ -1150,12 +1150,14 @@ def stateful_udf(
     expressions: list[PyExpr],
     return_dtype: PyDataType,
     resource_request: ResourceRequest | None,
-    init_args: tuple[tuple[Any, ...], dict[str, Any]] | None,
+    init_args: InitArgsType,
     batch_size: int | None,
     concurrency: int | None,
 ) -> PyExpr: ...
 def check_column_name_validity(name: str, schema: PySchema): ...
-def extract_partial_stateful_udf_py(expression: PyExpr) -> dict[str, PartialStatefulUDF]: ...
+def extract_partial_stateful_udf_py(
+    expression: PyExpr,
+) -> dict[str, tuple[PartialStatefulUDF, InitArgsType]]: ...
 def bind_stateful_udfs(expression: PyExpr, initialized_funcs: dict[str, Callable]) -> PyExpr: ...
 def resolve_expr(expr: PyExpr, schema: PySchema) -> tuple[PyExpr, PyField]: ...
 def hash(expr: PyExpr, seed: Any | None = None) -> PyExpr: ...

daft/runners/pyrunner.py

@@ -138,10 +138,15 @@
 
             logger.info("Initializing stateful UDFs: %s", ", ".join(partial_stateful_udfs.keys()))
 
-            # TODO: Account for Stateful Actor initialization arguments as well as user-provided batch_size
-            PyActorPool.initialized_stateful_udfs_process_singleton = {
-                name: partial_udf.func_cls() for name, partial_udf in partial_stateful_udfs.items()
-            }
+            PyActorPool.initialized_stateful_udfs_process_singleton = {}
+            for name, (partial_udf, init_args) in partial_stateful_udfs.items():
+                if init_args is None:
+                    PyActorPool.initialized_stateful_udfs_process_singleton[name] = partial_udf.func_cls()
+                else:
+                    args, kwargs = init_args
+                    PyActorPool.initialized_stateful_udfs_process_singleton[name] = partial_udf.func_cls(
+                        *args, **kwargs
+                    )
 
     @staticmethod
     def build_partitions_with_stateful_project(

daft/runners/ray_runner.py

@@ -931,9 +931,14 @@
             for name, psu in extract_partial_stateful_udf_py(expr._expr).items()
         }
         logger.info("Initializing stateful UDFs: %s", ", ".join(partial_stateful_udfs.keys()))
-        self.initialized_stateful_udfs = {
-            name: partial_udf.func_cls() for name, partial_udf in partial_stateful_udfs.items()
-        }
+
+        self.initialized_stateful_udfs = {}
+        for name, (partial_udf, init_args) in partial_stateful_udfs.items():
+            if init_args is None:
+                self.initialized_stateful_udfs[name] = partial_udf.func_cls()
+            else:
+                args, kwargs = init_args
+                self.initialized_stateful_udfs[name] = partial_udf.func_cls(*args, **kwargs)
 
     @ray.method(num_returns=2)
     def run(

daft/udf.py

@@ -4,7 +4,7 @@
 import functools
 import inspect
 from abc import abstractmethod
-from typing import Any, Callable, Union
+from typing import Any, Callable, Dict, Optional, Tuple, Union
 
 from daft.context import get_context
 from daft.daft import PyDataType, ResourceRequest
@@ -13,6 +13,7 @@
 from daft.expressions import Expression
 from daft.series import PySeries, Series
 
+InitArgsType = Optional[Tuple[Tuple[Any, ...], Dict[str, Any]]]
 UserProvidedPythonFunction = Callable[..., Union[Series, "np.ndarray", list]]
 
 
@@ -294,7 +295,7 @@ class StatefulUDF(UDF):
     name: str
     cls: type
     return_dtype: DataType
-    init_args: tuple[tuple[Any, ...], dict[str, Any]] | None = None
+    init_args: InitArgsType = None
     concurrency: int | None = None
 
     def __post_init__(self):

src/common/daft-config/src/python.rs

@@ -27,13 +27,21 @@ impl PyDaftPlanningConfig {
         }
     }
 
-    fn with_config_values(&mut self, default_io_config: Option<PyIOConfig>) -> PyResult<Self> {
+    fn with_config_values(
+        &mut self,
+        default_io_config: Option<PyIOConfig>,
+        enable_actor_pool_projections: Option<bool>,
+    ) -> PyResult<Self> {
         let mut config = self.config.as_ref().clone();
 
         if let Some(default_io_config) = default_io_config {
             config.default_io_config = default_io_config.config;
         }
 
+        if let Some(enable_actor_pool_projections) = enable_actor_pool_projections {
+            config.enable_actor_pool_projections = enable_actor_pool_projections;
+        }
+
         Ok(Self {
             config: Arc::new(config),
         })

src/daft-dsl/src/functions/python/mod.rs

@@ -9,6 +9,8 @@
 use common_treenode::{TreeNode, TreeNodeRecursion};
 use daft_core::datatypes::DataType;
 use itertools::Itertools;
+#[cfg(feature = "python")]
+use pyo3::{Py, PyAny};
 pub use runtime_py_object::RuntimePyObject;
 use serde::{Deserialize, Serialize};
 pub use udf_runtime_binding::UDFRuntimeBinding;
@@ -180,7 +182,7 @@
 #[cfg(feature = "python")]
 pub fn bind_stateful_udfs(
     expr: ExprRef,
-    initialized_funcs: &HashMap<String, pyo3::Py<pyo3::PyAny>>,
+    initialized_funcs: &HashMap<String, Py<PyAny>>,
 ) -> DaftResult<ExprRef> {
     expr.transform(|e| match e.as_ref() {
         Expr::Function {
@@ -213,20 +215,29 @@
 
 /// Helper function that extracts all PartialStatefulUDF python objects from a given expression tree
 #[cfg(feature = "python")]
-pub fn extract_partial_stateful_udf_py(expr: ExprRef) -> HashMap<String, pyo3::Py<pyo3::PyAny>> {
+pub fn extract_partial_stateful_udf_py(
+    expr: ExprRef,
+) -> HashMap<String, (Py<PyAny>, Option<Py<PyAny>>)> {
     let mut py_partial_udfs = HashMap::new();
     expr.apply(|child| {
         if let Expr::Function {
             func:
                 FunctionExpr::Python(PythonUDF::Stateful(StatefulPythonUDF {
                     name,
                     stateful_partial_func: py_partial_udf,
+                    init_args,
                     ..
                 })),
             ..
         } = child.as_ref()
         {
-            py_partial_udfs.insert(name.as_ref().to_string(), py_partial_udf.as_ref().clone());
+            py_partial_udfs.insert(
+                name.as_ref().to_string(),
+                (
+                    py_partial_udf.as_ref().clone(),
+                    init_args.clone().map(|x| x.as_ref().clone()),
+                ),
+            );
         }
         Ok(TreeNodeRecursion::Continue)
     })

src/daft-dsl/src/python.rs

@@ -222,7 +222,9 @@ pub fn stateful_udf(
 
 /// Extracts the `class PartialStatefulUDF` Python objects that are in the specified expression tree
 #[pyfunction]
-pub fn extract_partial_stateful_udf_py(expr: PyExpr) -> HashMap<String, Py<PyAny>> {
+pub fn extract_partial_stateful_udf_py(
+    expr: PyExpr,
+) -> HashMap<String, (Py<PyAny>, Option<Py<PyAny>>)> {
     use crate::functions::python::extract_partial_stateful_udf_py;
     extract_partial_stateful_udf_py(expr.expr)
 }

tests/expressions/test_udf.py

@@ -4,7 +4,9 @@
 import pyarrow as pa
 import pytest
 
+import daft
 from daft import col
+from daft.context import get_context, set_planning_config
 from daft.datatype import DataType
 from daft.expressions import Expression
 from daft.expressions.testing import expr_structurally_equal
@@ -13,6 +15,21 @@
 from daft.udf import udf
 
 
+@pytest.fixture(scope="function", params=[False, True])
+def actor_pool_enabled(request):
+    if request.param and get_context().daft_execution_config.enable_native_executor:
+        pytest.skip("Native executor does not support stateful UDFs")
+
+    original_config = get_context().daft_planning_config
+    try:
+        set_planning_config(
+            config=get_context().daft_planning_config.with_config_values(enable_actor_pool_projections=request.param)
+        )
+        yield request.param
+    finally:
+        set_planning_config(config=original_config)
+
+
 def test_udf():
     table = MicroPartition.from_pydict({"a": ["foo", "bar", "baz"]})
 
@@ -30,8 +47,8 @@ def repeat_n(data, n):
 
 
 @pytest.mark.parametrize("batch_size", [None, 1, 2, 3, 10])
-def test_class_udf(batch_size):
-    table = MicroPartition.from_pydict({"a": ["foo", "bar", "baz"]})
+def test_class_udf(batch_size, actor_pool_enabled):
+    df = daft.from_pydict({"a": ["foo", "bar", "baz"]})
 
     @udf(return_dtype=DataType.string(), batch_size=batch_size)
     class RepeatN:
@@ -41,18 +58,21 @@ def __init__(self):
         def __call__(self, data):
             return Series.from_pylist([d.as_py() * self.n for d in data.to_arrow()])
 
+    if actor_pool_enabled:
+        RepeatN = RepeatN.with_concurrency(1)
+
     expr = RepeatN(col("a"))
-    field = expr._to_field(table.schema())
+    field = expr._to_field(df.schema())
     assert field.name == "a"
     assert field.dtype == DataType.string()
 
-    result = table.eval_expression_list([expr])
+    result = df.select(expr)
     assert result.to_pydict() == {"a": ["foofoo", "barbar", "bazbaz"]}
 
 
 @pytest.mark.parametrize("batch_size", [None, 1, 2, 3, 10])
-def test_class_udf_init_args(batch_size):
-    table = MicroPartition.from_pydict({"a": ["foo", "bar", "baz"]})
+def test_class_udf_init_args(batch_size, actor_pool_enabled):
+    df = daft.from_pydict({"a": ["foo", "bar", "baz"]})
 
     @udf(return_dtype=DataType.string(), batch_size=batch_size)
     class RepeatN:
@@ -62,24 +82,27 @@ def __init__(self, initial_n: int = 2):
         def __call__(self, data):
             return Series.from_pylist([d.as_py() * self.n for d in data.to_arrow()])
 
+    if actor_pool_enabled:
+        RepeatN = RepeatN.with_concurrency(1)
+
     expr = RepeatN(col("a"))
-    field = expr._to_field(table.schema())
+    field = expr._to_field(df.schema())
     assert field.name == "a"
     assert field.dtype == DataType.string()
-    result = table.eval_expression_list([expr])
+    result = df.select(expr)
     assert result.to_pydict() == {"a": ["foofoo", "barbar", "bazbaz"]}
 
     expr = RepeatN.with_init_args(initial_n=3)(col("a"))
-    field = expr._to_field(table.schema())
+    field = expr._to_field(df.schema())
     assert field.name == "a"
     assert field.dtype == DataType.string()
-    result = table.eval_expression_list([expr])
+    result = df.select(expr)
     assert result.to_pydict() == {"a": ["foofoofoo", "barbarbar", "bazbazbaz"]}
 
 
 @pytest.mark.parametrize("batch_size", [None, 1, 2, 3, 10])
-def test_class_udf_init_args_no_default(batch_size):
-    table = MicroPartition.from_pydict({"a": ["foo", "bar", "baz"]})
+def test_class_udf_init_args_no_default(batch_size, actor_pool_enabled):
+    df = daft.from_pydict({"a": ["foo", "bar", "baz"]})
 
     @udf(return_dtype=DataType.string(), batch_size=batch_size)
     class RepeatN:
@@ -89,18 +112,21 @@ def __init__(self, initial_n):
         def __call__(self, data):
             return Series.from_pylist([d.as_py() * self.n for d in data.to_arrow()])
 
+    if actor_pool_enabled:
+        RepeatN = RepeatN.with_concurrency(1)
+
     with pytest.raises(ValueError, match="Cannot call StatefulUDF without initialization arguments."):
         RepeatN(col("a"))
 
     expr = RepeatN.with_init_args(initial_n=2)(col("a"))
-    field = expr._to_field(table.schema())
+    field = expr._to_field(df.schema())
     assert field.name == "a"
     assert field.dtype == DataType.string()
-    result = table.eval_expression_list([expr])
+    result = df.select(expr)
     assert result.to_pydict() == {"a": ["foofoo", "barbar", "bazbaz"]}
 
 
-def test_class_udf_init_args_bad_args():
+def test_class_udf_init_args_bad_args(actor_pool_enabled):
     @udf(return_dtype=DataType.string())
     class RepeatN:
         def __init__(self, initial_n):
@@ -109,10 +135,37 @@ def __init__(self, initial_n):
         def __call__(self, data):
             return Series.from_pylist([d.as_py() * self.n for d in data.to_arrow()])
 
+    if actor_pool_enabled:
+        RepeatN = RepeatN.with_concurrency(1)
+
     with pytest.raises(TypeError, match="missing a required argument: 'initial_n'"):
         RepeatN.with_init_args(wrong=5)
 
 
+@pytest.mark.parametrize("concurrency", [1, 2, 4])
+@pytest.mark.parametrize("actor_pool_enabled", [True], indirect=True)
+def test_stateful_udf_concurrency(concurrency, actor_pool_enabled):
+    df = daft.from_pydict({"a": ["foo", "bar", "baz"]})
+
+    @udf(return_dtype=DataType.string(), batch_size=1)
+    class RepeatN:
+        def __init__(self):
+            self.n = 2
+
+        def __call__(self, data):
+            return Series.from_pylist([d.as_py() * self.n for d in data.to_arrow()])
+
+    RepeatN = RepeatN.with_concurrency(concurrency)
+
+    expr = RepeatN(col("a"))
+    field = expr._to_field(df.schema())
+    assert field.name == "a"
+    assert field.dtype == DataType.string()
+
+    result = df.select(expr)
+    assert result.to_pydict() == {"a": ["foofoo", "barbar", "bazbaz"]}
+
+
 def test_udf_kwargs():
     table = MicroPartition.from_pydict({"a": ["foo", "bar", "baz"]})
 
@@ -208,8 +261,8 @@ def full_udf(e_arg, val, kwarg_val=None, kwarg_ex=None):
         full_udf()
 
 
-def test_class_udf_initialization_error():
-    table = MicroPartition.from_pydict({"a": ["foo", "bar", "baz"]})
+def test_class_udf_initialization_error(actor_pool_enabled):
+    df = daft.from_pydict({"a": ["foo", "bar", "baz"]})
 
     @udf(return_dtype=DataType.string())
     class IdentityWithInitError:
@@ -219,9 +272,16 @@ def __init__(self):
         def __call__(self, data):
             return data
 
+    if actor_pool_enabled:
+        IdentityWithInitError = IdentityWithInitError.with_concurrency(1)
+
     expr = IdentityWithInitError(col("a"))
-    with pytest.raises(RuntimeError, match="UDF INIT ERROR"):
-        table.eval_expression_list([expr])
+    if actor_pool_enabled:
+        with pytest.raises(Exception):
+            df.select(expr).collect()
+    else:
+        with pytest.raises(RuntimeError, match="UDF INIT ERROR"):
+            df.select(expr).collect()
 
 
 def test_udf_equality():